Crate little_sorry 

Little Sorry

Regret minimization algorithms for finding Nash equilibrium strategies in imperfect-information games.

Available Algorithms

All algorithms implement the RegretMinimizer trait:

Type	Algorithm	Key Property
CfrPlusRegretMatcher	CFR+	Regret clipping at zero
DiscountedRegretMatcher	DCFR	Configurable time-based discounting
DcfrPlusRegretMatcher	DCFR+	DCFR discounting + CFR+ clipping
LinearCfrRegretMatcher	Linear CFR	Linear time-weighted regrets
PcfrPlusRegretMatcher	PCFR+	Predictive future regret estimates
PdcfrPlusRegretMatcher	PDCFR+	DCFR+ discounting + predictive updates

Quick Start

use little_sorry::{CfrPlusRegretMatcher, RegretMinimizer};

let mut matcher = CfrPlusRegretMatcher::new(3);
for _ in 0..1000 {
    matcher.update_regret(&[1.0, -0.5, 0.2]);
}
let strategy = matcher.best_weight();
assert!((strategy.iter().sum::<f32>() - 1.0).abs() < 1e-6);

Re-exports

pub use cfr_plus::CfrPlusRegretMatcher;

pub use dcfr::DiscountedRegretMatcher;

pub use dcfr_plus::DcfrPlusRegretMatcher;

pub use discount::DiscountParams;

pub use linear_cfr::LinearCfrRegretMatcher;

pub use pcfr_plus::PcfrPlusRegretMatcher;

pub use pdcfr_plus::PdcfrPlusRegretMatcher;

pub use regret_minimizer::RegretMinimizer;

pub use cfr_plus::CfrPlusRegretMatcher as RegretMatcher;

Modules

cfr_plus

CFR+ regret minimization implementation.

dcfr

Discounted CFR (DCFR) implementation.

dcfr_plus

DCFR+ (Discounted CFR+) implementation.

discount

Discount parameter configurations for DCFR variants.

linear_cfr

Linear CFR implementation.

pcfr_plus

PCFR+ (Predictive CFR+) implementation.

pdcfr_plus

PDCFR+ (Predictive Discounted CFR+) implementation.

regret_minimizer

Trait definition and shared helpers for regret minimization algorithms.